The Limits of Elites’ Partisan Motivated Reasoning During the COVID-19 Pandemic

80th Annual Midwest Political Science Association Conference

Zachary P Dickson & Tevfik Murat Yildirim

London School of Economics & University of Stavanger

Motivation


If we stop testing right now, we’d have very few cases (Former Pres. Trump; June 15, 2020)


Background

  • We borrow from the literature showing that exogenous shocks, such as extreme weather events, can change the way people think about issues (Baccini and Leemann 2021)
    • Risk realization and preference for risk reduction policies
  • We know that support for COVID-19 policies is a function of the perceived risk of the virus (Wise et al. 2020)
  • Hypothesis: COVID-19 infection will reduce legislators’ opposition to containment measures
    • COVID-19 infection forces legislators to update their risk perceptions
    • Legislators fall in line with the majority for strategic reasons – the gig is up

Methods

  • Data – tweets sent by elected representatives in the US House and Senate
    • 602 legislators (N infected: 230)
  • Identify 82,870 tweets about COVID-19 policies using keyword searches
  • Classification – opposition vs. neutral/support using BERT language model (Nguyen, Vu, and Nguyen 2020)
    • Hand-coded 10,000 tweets for fine-tuning (F1 score = 0.954)
    • Model publicly available on Hugging Face

Measurement

  • Monthly opposition/total tweets

Picture

Estimation

  • Staggered difference-in-differences with COVID-19 infection as treatment
    • Bayesian hierarchical models – Bernoulli trials with a logit link (Gelman et al. 2014)
      • controls for COVID-19, legislator characteristics (ideology, age, gender, party, etc.)
    • Doubly-robust group-time models (Callaway and Sant’Anna 2021)
      • controls for COVID-19

Results – Bayesian hierarchical models


Figure 2: Posterior distribution of treatment effect parameter (\(\theta\)). Picture

\(e^\theta = 0.683\), indicating a reduction of 31.7 percent in opposition to COVID-19 restrictions after infection.

Note: Full posterior distributions and plot traces are available in the Appendix D.

Results – Group-time estimates


Table 1: Doubly-robust estimation of COVID-19 infection on opposition to COVID-19 policies
Model 1 Model 2
\(ATT (g,t)\) by dynamic aggregation \(-3.555^{**}\)
(1.4578)
\(ATT (g,t)\) by dynamic aggregation \(-3.5443^{**}\)
(1.508)
Control Group Never treated Not yet treated


Note: Outcome variable is measured as a fraction and multiplied by 100. Mean expressed opposition from legislators in the control group (who were not infected with COVID-19) was about 10.06 percent, so a reduction of 3.5 percentage points indicates around a 35 percent decrease in the number of tweets expressing opposition.

Summary

  • Elected officials downplayed the potential threat posed by the virus
  • COVID-19 infection reduces legislators’ opposition to containment measures
    • Limit to the lengths elites will go to defend their party’s position
    • Affective tipping point – COVID-19 infection forces legislators to update their risk perceptions
    • Strategic behavior – legislators fall in line with the majority of their party
  • Directions for future research
    • Beyond extreme weather events & natural disasters – other exogenous shocks
    • Disentangel risk realization vs. strategic considerations

Thank you!

References

Baccini, Leonardo, and Lucas Leemann. 2021. “Do Natural Disasters Help the Environment? How Voters Respond and What That Means.” Political Science Research and Methods 9 (3): 468–84.
Callaway, Brantly, and Pedro HC Sant’Anna. 2021. “Difference-in-Differences with Multiple Time Periods.” Journal of Econometrics 225 (2): 200–230.
Flynn, DJ, Brendan Nyhan, and Jason Reifler. 2017. “The Nature and Origins of Misperceptions: Understanding False and Unsupported Beliefs about Politics.” Political Psychology 38: 127–50.
Gelman, Andrew, John B Carlin, Hal S Stern, and Donald B Rubin. 2014. Bayesian Data Analysis. 3rd Edition. Chapman & Hall/CRC.
Leeper, Thomas J, and Rune Slothuus. 2014. “Political Parties, Motivated Reasoning, and Public Opinion Formation.” Political Psychology 35: 129–56.
Nguyen, Dat Quoc, Thanh Vu, and Anh Tuan Nguyen. 2020. “Bertweet: a Pre-Trained Language Model For English Tweets.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 9–14.
Taber, Charles S, and Milton Lodge. 2006. “Motivated Skepticism in the Evaluation of Political Beliefs.” American Journal of Political Science 50 (3): 755–69.
Wise, Toby, Tomislav D Zbozinek, Giorgia Michelini, Cindy C Hagan, and Dean Mobbs. 2020. “Changes in Risk Perception and Self-Reported Protective Behaviour During the First Week of the Covid-19 Pandemic in the United States.” Royal Society Open Science 7 (9): 200742.

Some examples

Support: I will continue to lead our commonwealth as we navigate the federal, state, and local response to the health and economic impacts to the coronavirus. We must continue to practice social distancing to keep our nation and communities safe [Rep. Rob Wittman, May 28, 2020]

Opposition: The House has reinstated its draconian mask mandate once again. My amendment would end taxpayer funding for mask mandates in the capitol complex. Follow the science, no more mask mandates! [Rep. Bob Good, July 28, 2021]

Theoretical Framework

  • We build on the literature showing that exogenous shocks, such as extreme weather events, can change the way people think about issues (Baccini and Leemann 2021)
    • Risk realization and preference for risk reduction policies
  • We know that support for COVID-19 policies is a function of the perceived risk of the virus (Wise et al. 2020)
  • Hypothesis: COVID-19 infection will reduce legislators’ opposition to containment measures
    • COVID-19 infection forces legislators to update their risk perceptions
    • Legislators fall in line with the majority for strategic reasons – the gig is up

Background & Motivation

  • Political elites in Congress – especially those in the Republican Party – expressed skepticism toward COVID-19 policies and downplayed the severity of the pandemic
    • If we stop testing right now, we’d have very few cases (Former Pres. Trump; June 15, 2020)
    • These health care providers and others are reimbursed at a higher rate if COVID is tied to it, so what do you think they’re doing? (Sen Joni Ernst; Sept. 2, 2020)
  • Is there a limit?

Treatment Schedule

Figure 2: COVID-19 Infection in US Legislators. Picture

Research Questions

  • Does COVID-19 infection reduce legislators’ opposition to containment measures?
    • Is there a limit to the lengths elites will go to defend their party’s position?
    • How do elites’ attitudes toward COVID-19 policies change over time?

The Limits of Elites’ Partisan Motivated Reasoning | Dickson & Yildirim